The DOI matches its Crossref record; 63 of the 63 references it cites were found in a registry or an index.
A tutorial on multiobjective optimization: fundamentals and evolutionary methods
Open accessAbstract
In almost no other field of computer science, the idea of using bio-inspired search paradigms has been so useful as in solving multiobjective optimization problems. The idea of using a population of search agents that collectively approximate the Pareto front resonates well with processes in natural evolution, immune systems, and swarm intelligence. Methods such as NSGA-II, SPEA2, SMS-EMOA, MOPSO, and MOEA/D became standard solvers when it comes to solving multiobjective optimization problems. This tutorial will review some of the most important fundamentals in multiobjective optimization and then introduce representative algorithms, illustrate their working principles, and discuss their application scope. In addition, the tutorial will discuss statistical performance assessment. Finally, it highlights recent important trends and closely related research fields. The tutorial is intended for readers, who want to acquire basic knowledge on the mathematical foundations of multiobjective optimization and state-of-the-art methods in evolutionary multiobjective optimization. The aim is to provide a starting point for researching in this active area, and it should also help the advanced reader to identify open research topics.
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Cites
A fast and elitist multiobjective genetic algorithm: NSGA-II
Deb, K., Pratap, A., Agarwal, S., & Meyarivan, T. (2002) · IEEE Transactions on Evolutionary Computation · doi:10.1109/4235.996017 · Cited by 50,053
Multi-Objective Optimization Using Evolutionary Algorithms
Deb, K., & Kalyanmoy, D. (2001) · Andalas University Repository (Andalas University) · Cited by 14,918
MOEA/D: A Multiobjective Evolutionary Algorithm Based on Decomposition
Zhang, Q., & Li, H. (2007) · IEEE Transactions on Evolutionary Computation · doi:10.1109/tevc.2007.892759 · Cited by 10,069
Multiobjective evolutionary algorithms: a comparative case study and the strength Pareto approach
Zitzler, E., & Thiele, L. (1999) · IEEE Transactions on Evolutionary Computation · doi:10.1109/4235.797969 · Cited by 8,798
Deb, K., & Jain, H. (2014) · IEEE Transactions on Evolutionary Computation · doi:10.1109/tevc.2013.2281535 · Cited by 7,133
Bäck, T. (1996) · Oxford University Press eBooks · doi:10.1093/oso/9780195099713.001.0001 · Cited by 5,113
SPEA2: Improving the strength pareto evolutionary algorithm
Zitzler, E., Laumanns, M., & Thiele, L. (2001) · doi:10.3929/ethz-a-004284029 · Cited by 5,008
Nonlinear Multiobjective Optimization
Miettinen, K. (1998) · International Series in Operations Research & Management Science · doi:10.1007/978-1-4615-5563-6 · Cited by 4,710
Multiple Criteria Decision Analysis: State of the Art Surveys
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Performance assessment of multiobjective optimizers: an analysis and review
Zitzler, E., Thiele, L., Laumanns, M., Fonseca, C. M., & da Fonseca, V. G. (2003) · IEEE Transactions on Evolutionary Computation · doi:10.1109/tevc.2003.810758 · Cited by 4,330
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A review on genetic algorithm: past, present, and future
Katoch, S., Chauhan, S. S., & Kumar, V. (2021) · Multimedia Tools and Applications · doi:10.1007/s11042-020-10139-6 · Cited by 4,655
A Comprehensive Review on Multi-objective Optimization Techniques: Past, Present and Future
Sharma, S., & Kumar, V. (2022) · Archives of Computational Methods in Engineering · doi:10.1007/s11831-022-09778-9 · Cited by 455
Performance assessment of the metaheuristic optimization algorithms: an exhaustive review
Halim, A. H., Ismail, I., & Das, S. (2021) · Artificial Intelligence Review · doi:10.1007/s10462-020-09906-6 · Cited by 293
Bias Mitigation for Machine Learning Classifiers: A Comprehensive Survey
Hort, M., Chen, Z., Zhang, J. M., Harman, M., & Sarro, F. (2022) · arXiv · doi:10.48550/arxiv.2207.07068 · Cited by 222
Cost-aware Multi-objective Bayesian optimisation
Abdolshah, M., Shilton, A., Rana, S., Gupta, S., & Venkatesh, S. (2019) · arXiv · doi:10.48550/arxiv.1909.03600 · Cited by 12
Metadata
- Authors
- Emmerich, M. T. M., & Deutz, A. H.
- Venue
- Natural Computing
- Published
- 2018-05-31
- Volume
- 17
- Issue
- 3
- Pages
- 585-609
- ISSN
- 1567-7818 (ISSN-L)
- 1572-9796
- OpenAlex
- W2807637204
- Cited by
- 731
- References
- 63
- FWCI
- 41.72
- Type
- journal article
- Language
- en
- Open access
- hybrid
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